A new method for evaluating the distribution of aggregate claims

In the present paper, we propose a method of practical utility for calculating the aggregate claims distribution in a discrete framework. It is an approximated method but unlike the other approximated methods proposed in the literature: the approximation concerns both the counting distribution and the convolution of the severity distributions; the approximation does not consist in truncating the original distribution up to a given number of terms nor in replacing it with another distribution or a more general function (but simply in considering only the significant numerical realizations and in neglecting the others); the resulting approximation of the aggregate claims distribution is lower than a prefixed maximum error (10(-6) in our applications). In particular, the probability distribution and also the first three moments are exact with the prefixed maximum error. The proposed method does not require special assumptions on the counting distribution nor the identical distribution of the severity random variables and it does not incur in underflow and overflow computational problems. It proves to be more flexible, easier and cheaper than the (exact and approximated) methods using recursion and Fast Fourier Transform. We show some applications using both a Poisson distribution and a Generalized Pareto mixture of Poisson distributions as counting distribution. In addition to the specific application proposed in this paper, the method can be applied in many other (life and nonlife) actuarial fields where the sum of discrete random variables and the calculation of compound distributions are involved. Besides, it can be extended in multivariate cases. (c) 2005 Elsevier Inc. All rights reserved

Laboratory Procedure for Measuring the Effectiveness of Dust Control Palliatives

Creation of fugitive dust on unpaved roads results in the loss of up to 25 mm (one inch) of surface aggregate annually (FHWA, 1998). On these roads, shearing forces created by vehicles dislodge the fine aggregate fraction (silt and clay) that binds the coarse aggregate. Turbulent airflow created by vehicles loft these fine particles in plumes of fugitive dust that impact health, safety, and quality of life. The loss of these particles results in raveling of the road surface, culminating in large annual losses of surface aggregate. Chemical dust control (palliatives) is an attractive option. However, there are currently no accepted field or laboratory performance testing procedures for chemical road dust palliatives. The lack of a method to predict palliative performance forces engineers and road managers into a trial-and-error methodology or reliance on personal judgment and supplier claims to determine what will work best on their unpaved road or runway surfaces. The overall objective of this research was to finalize the development of a laboratory test procedure for evaluating different dust control formulations and application rates required to effectively control the airborne suspension of dust particles in the size range (aerodynamic diameter) of 10 μm or less.Pacific Northwest Transportation Consortiu

The Role of Direct-Injury Government-Entity Lawsuits in the Opioid Litigation

The opioid epidemic has ravaged the United States, killing over 100 Americans every day and costing the nation upward of $90 billion a year. All branches and levels of the government have pursued measures to combat the epidemic and reduce its societal costs. Perhaps the most interesting response is the emergence of direct-injury government-entity lawsuits, which seek to recover damages from opioid companies that facilitated prescription pill addictions. Cities, counties, and states across the country are suing opioid manufacturers and distributors in unprecedented numbers. This Note explores the role of direct-injury government-entity claims as compared to other forms of civil litigation employed in the opioid crisis. It highlights the obstacles faced by parens patriae actions, individual lawsuits, class actions, and aggregate actions in general. This Note argues that direct-injury government claims have important advantages over other forms of civil litigation because they overcome certain defenses related to victim blameworthiness and because they function as inherently representative actions that bypass the certification requirements of traditional aggregate actions

Dynamic Allocation and Pricing in Incomplete Markets: A Survey

This paper surveys the recent development of empirical and theoretical researches on incomplete markets, pointing out the following aspects. First, the theoretical study in this field is motivated by empirical findings of both asset pricing anomalies and heterogeneous behavior among economic agents. Second, incomplete insurance combined with either borrowing constraints or transaction costs offers predictions consistent with empirical findings. In addition, the failure of insuring persistent or permanent shocks alone yields empirically reasonable predictions. Third, recent theoretical research has made attempts to endogenize incomplete insurance from first principles. Fourth, incomplete markets may make aggregate shocks distributed disproportionately among agents, thereby having a significant impact on dynamic allocation and pricing. Finally the theoretical research into incomplete markets triggers a reassessment of welfare implications as to business cycles, economic growth, and financial integration.

Taking Risks Behind the Veil of Ignorance

A natural view in distributive ethics is that everyone's interests matter, but the interests of the relatively worse off matter more than the interests of the relatively better off. I provide a new argument for this view. The argument takes as its starting point the proposal, due to Harsanyi and Rawls, that facts about distributive ethics are discerned from individual preferences in the "original position." I draw on recent work in decision theory, along with an intuitive principle about risk-taking, to derive the view

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility